Exercism VS Activeloop
Compare Exercism VS Activeloop and see what are their differences
AI agents that spec, build, test, and ship code โ with voice chat, deep GitHub integration, and zero LLM markup.
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Exercism
Codecademy
edX
Treehouse
Hackr.io
Free Code Camp
Pantheon
Pluralsight
Activeloop
Iterative.ai
Pachyderm
Scale
DoltHub
Snowflakepowe.red
Activeloop provides an optimized format for unstructured data, so users can stream their machine learning datasets while training ML models in PyTorch and TensorFlow. Activeloop acts as a data lake for deep learning on unstructured data and offers in-browser dataset visualization, querying, and version control. On top of those features, Activeloop integrates with experimentation and labeling tools to allow rapid iteration on computer vision datasets.
Machine Learning teams can apply Activeloop's data infrastructure to ship their models fast in the following use cases:
Exercism ActiveloopNo features have been listed yet.
Based on our record, Exercism seems to be a lot more popular than Activeloop. While we know about 318 links to Exercism, we've tracked only 4 mentions of Activeloop. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Exercism.org structured deliberate practice, no AI required. - Source: dev.to / 3 months ago
Providing free coding exercises and mentorship, Exercism helps developers practice and improve their programming skills step by step. Their Python Track offers a series of exercises that guide learners from beginner to more advanced levels. - Source: dev.to / 6 months ago
Exercism is a code practice + mentoring platform in 74 languages. Why it can work: although it is not exclusively focused on groups of five, its mentoring and peer review model allows forming mini-circles where participants give each other mutual feedback. Limitation: it is not originally intended as a "venting circle" (My favorite), but more technical-practical. - Source: dev.to / 8 months ago
Keep practicing, keep coding, and most importantlyโhave fun with it! If you enjoyed this exercise, explore more challenges on exercism to continue building your Go skills. As always, you can find the complete code in my GitHub repo: https://github.com/RubenOAlvarado/algorithms. - Source: dev.to / 9 months ago
(concepts/topics) : The New Turing Omnibus, 66 Excursions in Computer Science[1] Code Complete [2] Debugging The 9 Indispensable Rules of Finding Even the Most Elusive Software and Hardware Problems [3] Code: The Hidden Language of Computer Hardware and Software [4] -- backround stories on how 'computer' things came to be -------- [1] : https://www.amazon.com/New-Turing-Omnibus-Sixty-Six-Excursions/dp/0805071660... - Source: Hacker News / about 1 year ago
This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input. Source: about 3 years ago
u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :). Source: about 4 years ago
Hub, our open-source package, lets you stream datasets while training to PyTorch/TensorFlow. Check out how we achieved 95% GPU utilization while training on ImageNet at 50% less cost. We're building the Database for AI, with everything it should contain. If there's an adjacent feature that would make it more useful for your workflow, do let us know! Source: over 4 years ago
I'm Davit from Activeloop (activeloop.ai). Source: over 4 years ago
Codecademy - Learn the technical skills you need for the job you want. As leaders in online education and learning to code, weโve taught over 45 million people using a tested curriculum and an interactive learning environment.
Iterative.ai - Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.
edX - Best Courses. Top Institutions. Learn anytime, anywhere.
Pachyderm - Pachyderm is an open source analytics engine that uses Docker containers for distributed computations.
Treehouse - Treehouse is an award-winning online platform that teaches people how to code.
Scale - Get human tasks done with just one line of code.